Red Hat Data Grid

Most of the time, when we think about collecting, parsing and storing Logs, the first thing that pops in our mind is the ElasticStack or ELK. It is well positioned in developer and sysadmin's minds. The stack combines the popular Elasticsearch, Logstash and Kibana projects together to easy the collection/aggregation, store, and visualization of application logs. As an Apache Camel rider and Infinispan enthusiast, I prepared this exercise to produce my own log collector and store stack using Red Hat's...

This article describes how to run a client-server application for JBoss Data Grid on Openshift using Red Hat Container Development Kit 3.0 Beta and Minishift. This environment for this tutorial can be set up quickly following up this previous post on the Developer Blog. First of all, you need to make sure you have available the ImageStreams and Templates, in order to run JBoss Data Grid in your Openshift Paas. You can check that your environment contains both of them...

We're excited to announce the availability of Red Hat JBoss Data Grid (JDG) Version 7.1. Thanks and congratulations to the JDG engineering and product management team for this release. JDG 7.1 release focuses on the following areas: Performance enhancements Apache Spark 2.x integration Several other enhancements The following new features were added in support of these themes: Release Highlights Performance enhancements JDG 7.1 features core performance improvements, especially in clustered write operations. Current tests have shown up to 60% increase...

Expanding on Tristan's blog, where he spoke of enabling security for JBoss Data Grid caches, in this post we will cover how to add LDAP based security to the JDG caches. The principles and techniques remain defined by Tristan, but there are some minor changes that I will be highlighting in this blog for a successful working configuration of JDG enabled with LDAP security. Before we jump on to configuring the JDG for security, I would like to brush up...

An in-memory data grid is a distributed data management platform for application data that: Uses memory (RAM) to store information for very fast, low-latency response time, and very high throughput. Keeps copies of that information synchronized across multiple servers for continuous availability, information reliability, and linear scalability. Can be used as distributed cache, NoSQL database, event broker, compute grid, and Apache Spark data store. The technical advantages of an in-memory data grid (IMDGs) provide business benefits in the form of...

Red Hat JBoss Data Virtualization (JDV) provides several capabilities for caching data including: materialized views, result set caching, and code table caching. These techniques can be used to significantly improve performance in many situations. With the exception of external materialized views, the cached data is accessed through the BufferManager. For better performance, the BufferManager setting should be adjusted to the memory constraints of your installation. See the Admin Guide for more on parameter tuning. JDV supports two kinds of caching...

Web developers and IT professionals are the foundations of any quality business’ data security. However, with technology constantly changing and evolving as well as becoming more consumer-friendly, this data’s vulnerability only increases and it can often be hard to even notice how this new technology can actually affect your company until it occurs. Despite this, ignorance to modern hacking techniques does not refute their inability to transform even the smallest of devices into a weapon with which to infect or...

Welcome to another episode of the series: “Unlock your Red Hat JBoss Data Grid (JDG) data with Red Hat JBoss Data Virtualization (JDV).” This post will guide you through an example of connecting to Red Hat JBoss Data Grid data source, using Teiid Designer. In this example, we will demonstrate connecting to a local JDG data source. We’re using the JDG 6.6.1, but you can connect to any local or remote JDG source (version 6.6.1) if you wish, using the...

Introduction: A feature of OpenShift is jobs and today I will be explaining how you can use jobs to run your spark machine, learning data science applications against Spark running on OpenShift. You can run jobs as a batch or scheduled, which provides cron like functionality. If jobs fail, by default OpenShift will retry the job creation again. At the end of this article, I have a video demonstration of running spark jobs from OpenShift templates against Spark running on...

Do you like JavaEE apps, but wonder how to fit them into a microservices-centric workflow? WildFly Swarm is the answer. I know—“Java” and “microservices” are not words that seem to go together. Java is an old, relatively unsexy programming language. It’s a pretty useful one, but it was created long before the era of Continuous Delivery, containers and microservices. But that doesn’t mean you have to give up on Java if you want to take advantage of microservices. WildFly Swarm...

We are very excited to announce General Availability (GA) of Red Hat JBoss Data Grid (JDG) 7! JDG supercharges today’s modern applications and allows developers to meet tough requirements of high performance, availability, reliability, and elastic scale. JBoss Data Grid is compatible with the existing data tier as well as applications written in any language, using any framework and any platform via multiple APIs such as memcached, HotRod, and REST. Red Hat JBoss Data Grid empowers developers to obtain a...

At DevNation, Red Hat's Galder Zamarreño gave a talk with a live demo, Building reactive applications with Node.js and Red Hat JBoss Data Grid. The demo consisted of building an event-based three tier web application using JBoss Data Grid (JDG) as the data layer, an event manager running on Node.js, and a web client. Recently, support for Node.js clients was added to JDG, opening up the performance of a horizontally scalable in-memory data grid, to reactive web and mobile applications...